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        <span>标签和特征长度处理</span>
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        <h1 id="标签和特征长度处理"><a href="#标签和特征长度处理" class="headerlink" title="标签和特征长度处理"></a>标签和特征长度处理</h1><p>在进行机器学习之前需要查看标签和特征的长度，判断是否相等，数据比标签少30s</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/6/8</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"><span class="string">&quot;&quot;&quot;</span></span><br><span class="line"><span class="string">判断：</span></span><br><span class="line"><span class="string">读取注释和特征查看长度是否相等</span></span><br><span class="line"><span class="string">&quot;&quot;&quot;</span></span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line"><span class="comment"># 忽略警告</span></span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">1</span>, <span class="number">19</span>):</span><br><span class="line">    feature = pd.read_excel(<span class="string">&#x27;F:/py/python-ECG信号处理/features_data&#x27;</span> + <span class="string">&#x27;/slp&#x27;</span> + <span class="string">&#x27;%s&#x27;</span> % i + <span class="string">&#x27;.xlsx&#x27;</span>)</span><br><span class="line">    data = pd.get_dummies(feature.iloc[<span class="number">0</span>:<span class="built_in">len</span>(feature), <span class="number">1</span>:])</span><br><span class="line">    note = pd.read_excel(<span class="string">&#x27;F:/py/python-ECG信号处理/note_data&#x27;</span> + <span class="string">&#x27;/%s&#x27;</span> % i + <span class="string">&#x27;.xlsx&#x27;</span>)</span><br><span class="line">    tag = pd.get_dummies(note.iloc[<span class="number">0</span>:<span class="built_in">len</span>(note)<span class="number">-1</span>, <span class="number">-1</span>:])</span><br><span class="line">    <span class="keyword">if</span> <span class="built_in">len</span>(data) == <span class="built_in">len</span>(tag):</span><br><span class="line">        <span class="keyword">pass</span></span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        print(<span class="string">f&#x27;数据slp<span class="subst">&#123;i&#125;</span>不相等&#x27;</span>)</span><br><span class="line">        print(<span class="string">f&#x27;特征长度<span class="subst">&#123;<span class="built_in">len</span>(data)&#125;</span>, 标签长度‘<span class="subst">&#123;<span class="built_in">len</span>(tag)&#125;</span>&#x27;</span>)</span><br><span class="line"><span class="keyword">else</span>:</span><br><span class="line">    print(<span class="string">f&#x27;检测完毕&#x27;</span>)</span><br></pre></td></tr></table></figure>

<h2 id="检测结果"><a href="#检测结果" class="headerlink" title="检测结果"></a>检测结果</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br></pre></td><td class="code"><pre><span class="line">数据slp5不相等	特征长度<span class="number">700</span>	标签长度<span class="number">719</span></span><br><span class="line">数据slp7不相等	特征长度<span class="number">719</span>	标签长度<span class="number">713</span></span><br><span class="line">数据slp8不相等	特征长度<span class="number">719</span>	标签长度<span class="number">693</span></span><br><span class="line">数据slp10不相等	特征长度<span class="number">699</span>	标签长度<span class="number">697</span></span><br><span class="line">数据slp14不相等	特征长度<span class="number">479</span>	标签长度<span class="number">457</span></span><br><span class="line">数据slp15不相等	特征长度<span class="number">699</span>	标签长度<span class="number">719</span></span><br><span class="line">数据slp16不相等	特征长度<span class="number">739</span>	标签长度<span class="number">719</span></span><br><span class="line">数据slp17不相等	特征长度<span class="number">439</span>	标签长度<span class="number">438</span></span><br><span class="line">检测完毕</span><br></pre></td></tr></table></figure>

<h2 id="数据处理"><a href="#数据处理" class="headerlink" title="数据处理"></a>数据处理</h2><p>slp5和slp15为slp03和slp60，只清除了AHI标签，没有清理睡眠标签</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/6/8</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> wfdb</span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line"><span class="comment"># 忽略警告</span></span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 读取st文件</span></span><br><span class="line"><span class="comment"># test = input(&quot;请输入想读取的文件名： &quot;)</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># test = &#x27;slp03&#x27;</span></span><br><span class="line">test = <span class="string">&#x27;slp60&#x27;</span></span><br><span class="line">annotation = wfdb.rdann(<span class="string">&#x27;F:/slpdb_data/mitdata&#x27;</span> + <span class="string">&#x27;/%s&#x27;</span> % test, <span class="string">&#x27;st&#x27;</span>)</span><br><span class="line">aux = annotation.aux_note</span><br><span class="line"><span class="comment"># record = wfdb.rdrecord(&#x27;F:/slpdb_data/mitdata&#x27; + &#x27;/%s&#x27; % test, sampfrom=1222500, sampto=1237500, channels=[3])</span></span><br><span class="line"><span class="comment"># ecg_signal = record.p_signal</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 查看低通气和osa阻塞性呼吸暂停的ecg信号取别</span></span><br><span class="line"><span class="comment"># 对标签进行处理。分为2个类别,就是正常和低通气。把osa判定为低通气</span></span><br><span class="line"></span><br><span class="line">tag1 = []</span><br><span class="line">tag2 = []</span><br><span class="line">tag3 = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(aux)):</span><br><span class="line">    <span class="keyword">if</span> <span class="number">538</span> &lt; i &lt; <span class="number">549</span>:</span><br><span class="line">        <span class="keyword">pass</span></span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        <span class="keyword">if</span> (aux[i][<span class="number">0</span>] == <span class="string">&#x27;4&#x27;</span>) <span class="keyword">or</span> (aux[i][<span class="number">0</span>] == <span class="string">&#x27;3&#x27;</span>):</span><br><span class="line">            tag1.append(<span class="number">1</span>)</span><br><span class="line">            tag2.append(<span class="number">1</span>)</span><br><span class="line">            tag3.append(<span class="number">1</span>)</span><br><span class="line">        <span class="keyword">elif</span> aux[i][<span class="number">0</span>] == <span class="string">&#x27;2&#x27;</span>:</span><br><span class="line">            tag1.append(<span class="number">2</span>)</span><br><span class="line">            tag2.append(<span class="number">2</span>)</span><br><span class="line">            tag3.append(<span class="number">1</span>)</span><br><span class="line">        <span class="keyword">elif</span> aux[i][<span class="number">0</span>] == <span class="string">&#x27;1&#x27;</span>:</span><br><span class="line">            tag1.append(<span class="number">3</span>)</span><br><span class="line">            tag2.append(<span class="number">2</span>)</span><br><span class="line">            tag3.append(<span class="number">1</span>)</span><br><span class="line">        <span class="keyword">elif</span> aux[i][<span class="number">0</span>] == <span class="string">&#x27;R&#x27;</span>:</span><br><span class="line">            tag1.append(<span class="number">4</span>)</span><br><span class="line">            tag2.append(<span class="number">3</span>)</span><br><span class="line">            tag3.append(<span class="number">2</span>)</span><br><span class="line">        <span class="keyword">else</span>:</span><br><span class="line">            tag1.append(<span class="number">5</span>)</span><br><span class="line">            tag2.append(<span class="number">4</span>)</span><br><span class="line">            tag3.append(<span class="number">3</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 即aux[i][2]:为低通气这些的判定，如果没有则认定为正常，有就进行判定</span></span><br><span class="line">tag4 = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(aux)):</span><br><span class="line">    <span class="comment"># slp03</span></span><br><span class="line">    <span class="comment"># if 174 &lt; i &lt; 194:</span></span><br><span class="line">    <span class="keyword">if</span> <span class="number">538</span> &lt; i &lt; <span class="number">549</span>:</span><br><span class="line">        <span class="comment"># slp60</span></span><br><span class="line">        <span class="keyword">pass</span></span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        <span class="keyword">if</span> <span class="built_in">len</span>(aux[i]) == <span class="number">1</span> <span class="keyword">or</span> <span class="built_in">len</span>(aux[i]) == <span class="number">2</span>:</span><br><span class="line">            tag4.append(<span class="number">0</span>)</span><br><span class="line">        <span class="keyword">elif</span> <span class="built_in">len</span>(aux[i]) &gt;= <span class="number">3</span>:</span><br><span class="line">            <span class="keyword">if</span> (aux[i][<span class="number">2</span>] == <span class="string">&#x27;H&#x27;</span>) <span class="keyword">or</span> (aux[i][<span class="number">2</span>] == <span class="string">&#x27;X&#x27;</span>) <span class="keyword">or</span> (aux[i][<span class="number">2</span>] == <span class="string">&#x27;O&#x27;</span>) <span class="keyword">or</span> (aux[i][<span class="number">2</span>] == <span class="string">&#x27;C&#x27;</span>):</span><br><span class="line">                tag4.append(<span class="number">1</span>)</span><br><span class="line">            <span class="keyword">else</span>:</span><br><span class="line">                tag4.append(<span class="number">0</span>)</span><br><span class="line"></span><br><span class="line">label1 = pd.DataFrame(tag1, columns=[<span class="string">&#x27;N321RW&#x27;</span>])</span><br><span class="line">label2 = pd.DataFrame(tag2, columns=[<span class="string">&#x27;DLRW&#x27;</span>])</span><br><span class="line">label3 = pd.DataFrame(tag3, columns=[<span class="string">&#x27;NRW&#x27;</span>])</span><br><span class="line">label4 = pd.DataFrame(tag4, columns=[<span class="string">&#x27;AHI&#x27;</span>])</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">label = pd.concat([label1, label2, label3, label4], axis=<span class="number">1</span>)</span><br><span class="line">num = <span class="built_in">int</span>(<span class="built_in">input</span>(<span class="string">&quot;输入保存的文件名： &quot;</span>))</span><br><span class="line">label.to_excel(<span class="string">&#x27;%d&#x27;</span> % num + <span class="string">&quot;.xlsx&quot;</span>)</span><br><span class="line"></span><br></pre></td></tr></table></figure>

<h2 id="再次检测"><a href="#再次检测" class="headerlink" title="再次检测"></a>再次检测</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line">数据slp7不相等	特征长度<span class="number">719</span>	标签长度<span class="number">713</span></span><br><span class="line">数据slp8不相等	特征长度<span class="number">719</span>	标签长度<span class="number">693</span></span><br><span class="line">数据slp10不相等	特征长度<span class="number">699</span>	标签长度<span class="number">697</span></span><br><span class="line">数据slp14不相等	特征长度<span class="number">479</span>	标签长度<span class="number">457</span></span><br><span class="line">数据slp16不相等	特征长度<span class="number">739</span>	标签长度<span class="number">719</span></span><br><span class="line">数据slp17不相等	特征长度<span class="number">439</span>	标签长度<span class="number">438</span></span><br><span class="line">检测完毕</span><br></pre></td></tr></table></figure>

<h2 id="数据查看"><a href="#数据查看" class="headerlink" title="数据查看"></a>数据查看</h2><p>slp7即为slp14</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> wfdb</span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line"><span class="comment"># 忽略警告</span></span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 读取st文件</span></span><br><span class="line"><span class="comment"># test = input(&quot;请输入想读取的文件名： &quot;)</span></span><br><span class="line"></span><br><span class="line">test = <span class="string">&#x27;slp14&#x27;</span></span><br><span class="line">annotation = wfdb.rdann(<span class="string">&#x27;F:/slpdb_data/mitdata&#x27;</span> + <span class="string">&#x27;/%s&#x27;</span> % test, <span class="string">&#x27;st&#x27;</span>)</span><br><span class="line">aux = annotation.aux_note</span><br><span class="line">sample_min = annotation.sample.<span class="built_in">min</span>()</span><br><span class="line">sample_max = annotation.sample.<span class="built_in">max</span>()</span><br><span class="line">print(<span class="string">f&#x27;起始位置为<span class="subst">&#123;sample_min&#125;</span>, 终止位置为<span class="subst">&#123;sample_max&#125;</span>&#x27;</span>)</span><br><span class="line">起始位置为<span class="number">45000</span>, 终止位置为<span class="number">5392500</span></span><br></pre></td></tr></table></figure>

<p>slp8:slp16</p>
<p>起始位置为195000, 终止位置为5392500</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">int</span>(<span class="number">195000</span>/record.fs/<span class="number">30</span>), <span class="built_in">int</span>(record.sig_len/record.fs/<span class="number">30</span> - <span class="number">1</span>)):</span><br></pre></td></tr></table></figure>

<p>slp10:slp37</p>
<p>数据slp10不相等    特征长度699    标签长度697</p>
<p>起始位置为15000, 终止位置为5242500</p>
<p>slp14:slp59</p>
<p>起始位置为165000, 终止位置为3592500</p>
<p>slp16:slp61</p>
<p>数据slp16不相等    特征长度739    标签长度719</p>
<p>起始位置为150000, 终止位置为5542500</p>
<p>slp17:slp66</p>
<p>数据slp17不相等    特征长度439    标签长度438</p>
<p>起始位置为1, 终止位置为3285000</p>
<h2 id="数据检测完毕"><a href="#数据检测完毕" class="headerlink" title="数据检测完毕"></a>数据检测完毕</h2><p>以后5分钟的数据片段还是要经过这个处理，标签缺失</p>
<p><img src="http://img3.a0bi.com/upload/ttq/20150716/1437008701869.jpg"></p>

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